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Deploying big-data Machine Learning (ML) services in a cloud environment presents a challenge to the cloud vendor with respect to the cloud container configuration sizing for any given customer use case. OracleLabs has developed an…
Job schedulers are a key component of scalable computing infrastructures. They orchestrate all of the work executed on the computing infrastructure and directly impact the effectiveness of the system. Recently, job workloads have…
Within enterprises, there is a growing need to intelligently navigate data lakes, specifically focusing on data discovery. Of particular importance to enterprises is the ability to find related tables in data repositories. These tables can…
Many organizations routinely analyze large datasets using systems for distributed data-parallel processing and clusters of commodity resources. Yet, users need to configure adequate resources for their data processing jobs. This requires…
Recent advances in data analytics have enabled the accurate prediction of user access patterns, giving rise to the idea of packed caching delivering multiple co accessed data items together as a bundle. This improves caching efficiency, as…
RZBENCH is a benchmark suite that was specifically developed to reflect the requirements of scientific supercomputer users at the University of Erlangen-Nuremberg (FAU). It comprises a number of application and low-level codes under a…
Evaluating competing systems in a comparable way, i.e., benchmarking them, is an undeniable pillar of the scientific method. However, system performance is often summarized via a small number of metrics. The analysis of the evaluation…
Enterprises operate large data lakes using Hadoop and Spark frameworks that (1) run a plethora of tools to automate powerful data preparation/transformation pipelines, (2) run on shared, large clusters to (3) perform many different…
As machine learning systems are increasingly deployed in high-stakes domains such as criminal justice, finance, and healthcare, the demand for interpretable and trustworthy models has intensified. Despite the proliferation of local…
Domain-specific software and hardware co-design is encouraging as it is much easier to achieve efficiency for fewer tasks. Agile domain-specific benchmarking speeds up the process as it provides not only relevant design inputs but also…
Online Large Language Model (LLM) services such as ChatGPT and Claude 3 have transformed business operations and academic research by effortlessly enabling new opportunities. However, due to data-sharing restrictions, sectors such as…
Apache Kafka has become a foundational platform for high throughput event streaming, enabling real time analytics, financial transaction processing, industrial telemetry, and large scale data driven systems. Despite its maturity and…
The evolution of distributed architectures and programming paradigms for performance-oriented program development, challenge the state-of-the-art technology for performance tools. The area of high performance computing is rapidly expanding…
A data product is created with the intention of solving a specific problem, addressing a specific business usecase or meeting a particular need, going beyond just serving data as a raw asset. Data products enable end users to gain greater…
Big data is gaining overwhelming attention since the last decade. Almost all the fields of science and technology have experienced a considerable impact from it. The cloud computing paradigm has been targeted for big data processing and…
This paper presents our joint research efforts on big data benchmarking with several industrial partners. Considering the complexity, diversity, workload churns, and rapid evolution of big data systems, we take an incremental approach in…
Data processing engines increasingly leverage distributed file systems for scalable, cost-effective storage. While the Apache Parquet columnar format has become a popular choice for data storage and retrieval, the immutability of Parquet…
In April 2023, HEPScore23, the new benchmark based on HEP specific applications, was adopted by WLCG, replacing HEP-SPEC06. As part of the transition to the new benchmark, the CPU corepower published by the sites needed to be compared with…
Public cloud computing environments, such as Amazon AWS, Microsoft Azure, and the Google Cloud Platform, have achieved remarkable improvements in computational performance in recent years, and are also expected to be able to perform…
The paradigm of big data is characterized by the need to collect and process data sets of great volume, arriving at the systems with great velocity, in a variety of formats. Spark is a widely used big data processing system that can be…